OVERSHOOTING CONVECTIVE CLOUD TOP HEIGHT ANALYSIS OVER CENTRAL EUROPE , SUMMERS 2009 - 2011

OVERSHOOTING CONVECTIVE CLOUD TOP HEIGHT ANALYSIS OVER CENTRAL EUROPE, SUMMERS 2009-2011

Ján KaňákSlovak Hydrometeorological Institute Bratislava

[email protected]

Kristopher BedkaScience Systems & Applications, Inc. @ NASA Langley Research Center

[email protected]

Alois Sokol Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava

[email protected]

EUMETSAT Meteorological Satellite Conference, 3-7 September 2012, Sopot, Poland 1

Part 1

MotivationGeometrical backgroundViewMSG Measurement tool


Motivation (1)

Photo:30th May 200518:19 UTCBratislava

The storm captured in this photo was located over the borders between north Austria and south Moravia

A well-defined shadow was cast on cirrus clouds east of the storm from an overshooting cloud top (OT)

According to the satellite image, the storm from was about 80 km to the NW of Bratislava

Satellite image:MSG channel 12, HRVStart time: 18:15 UTC


Motivation (2)Overshooting tops (OTs) indicate the presence of a strong updraft. Severe weather and aviation turbulence often occur in the vicinity of the OT region.

OTs can be observed or inferred in visible and infrared satellite imagery- Fine temporal (<= 5 min) and spatial resolution (<= 1 km) satellite imagery are

needed to highlight important details on OT time evolution and updraft intensity

- Polar orbiting satellites provide fine spatial resolution but coarse time resolution

- Geostationary satellites can provide sufficient time sampling of OTs in rapid scan operations and, in general, are more suitable for continuous observation and detection of OTs over wide regions

Operational satellite-based cloud top height algorithms will often fail to assign representative heights to OT pixels because they are colder than any temperature represented in a nearby rawinsonde or NWP mode profile

- The tropopause height is used as the default OT height when IR BT < profile temp

Knowledge of the OT height is very important to the aviation industry as pilots face the choice of whether to fly above or around an OT. Satellite-derived cloud heights are one of the few sources of observational guidance in many regions



The shadows cast by OT features upon the surrounding cirrus anvil cloud in low solar elevation angle conditions can be used to estimate the magnitude of OT penetration into the upper troposphere/lower stratosphere (UTLS) region

The goal of this study is to utilize a combination of IR spatial gradients near OT signatures and the OT UTLS penetration magnitude observed by MSG SEVIRI to characterize the lapse rate associated with UTLS penetrating convection

It is the hope that cloud top height algorithm developers could use IR spatial gradients in combination with lapse rate information to assign more representative heights to OTs

Motivation (3)

Earth radius

Geometrical background

Sun rays

Cirrus layer

OT Sun elevation angle

Length of OT shadow

Earth surface

OT heightS (Shadow edge)

Method is applicable only in case oflow Sun elevation angles (lower then 25°), when the length of shadow is accurately measurable in 1 km HRV imagery

Cirrus layer heightabove the Earth

surface

To estimate OT height, we have to calculate Sun elevation angle at the OT position and to measure OT shadow length! Additionally we need to know value of Earth radius, mean land surface elevation above the sea level at the point S and cirrus layer height above the Earth surface. Sun elevation is calculated from apparent solar time at the measured point, right ascension and declination. Also parallax shift of OT top in the image should be considered to avoid uncertainties of the result.


Results: Sun elevation: 4,81° Shadow length: 59 pixels = 17 km OT height: 1464 m

b) Adjust Pointer length

Result: 1464m(height of OT)

a) Set Pointer elevation: 8055m a.s.l.

ViewMSG Measurement toolManual estimation of OT height above the anvil in two steps:


Part 2

Description of statistical data setData distributionsRelations between Minimum OT BT, Anvil BT and OT Height


We worked with two independent statistical data sets:

1. OTs detected by the Bedka et al (JAMC, 2010) algorithm- Detection is based on localization of the minimum OT BT, calculation of mean anvil BT (brightness temperature), calculation and thresholding of Min OT – anvil BT difference (OTs detected by IR).

2. OTs detected with ViewMSG measurement tool developed by Ján Kaňák- Detection is based on visual inspection of MSG HRV imagery, localization of OTs with well visible shadows over the anvils during low Sun elevation angles, calculation of shadow length and OT height (OTs detected by HRV).

Both data sets represent June-August 2009, 2010 and 2011.15-minute operational MSG SEVIRI IR 10,8µm and HRV channel observations were used

Description of statistical data setPart 1


OTs by IR algorithm:Date and timeLatitudeLongitudeMinimum OT brightness tempMinimum OT – Anvil BT differenceRelative number of anvil pixels

OTs by HRV algorithm:Date and timeLatitudeLongitudeApparent solar timeSun elevationOT height

Data processing: Co-location of OTs limited to 10 km

Resulting co-located pairs were further analyzed with the aim to find relations between anvil BT, OT BT, Sun elevation and OT height.

Month Num200906 776200907 714200908 830201006 704201007 463201008 242201106 611201107 376201108 361Total 5077

Month Num200906 4450200907 2353200908 1442201006 3262201007 1839201008 1110201106 3421201107 1126201108 1897Total 20900

Description of statistical data setPart 2


Data distributions (co-location difference)

During first co-location attempt 15kmco-location difference was used (blank columns).Distribution tail over 9,5km belongs to rangeout of MSG IR pixel native resolution.We decided to cut off this tail with co-locationthreshold of 10km.Distribution became more symmetric with meanco-location difference about 4,5km

Averaged co-location distance of selected datapairs finally reached MSG native resolution overthe region of central Europe for all processedmonths.

MSG IR pixel native resolution in CE Europe

Coarse visual OT identification approach wasused for months July, August 2010, 2011. Forthe rest of months identification methodologywas more precise. It is evident from lower meanco-location differences.


Important note: The next part of presentation will deal only with subset of co-located data cases. In chart below such cases are represented by green columns.

Data distributions

Final co-located dataset was filtered by: OT height < 3000m Sun Elevation <15° Fraction of pixels surrounding OT

considered anvil by Bedka method > 70%

Ratio of IR-HRV pairs number and total HRV detections is quite low. Reasons are not onlyfiltering, but also low efficiency of manual selection of OT with good visible shadows.


Data distributions (anvil BT and Minimum OT BT)

HRV method (blue) selects systematicallycolder OTs then IR method (red),Shift is about 2K.

HRV method (blue) selects systematicallycolder anvils then IR method (red),shift is about 2K.

220 K

212 K

218 K

210 K

In average OT is 8 degrees colder then anvil:(220-212 = 218-210 = 8K)

Relative counting rate was used because of different total number of items in IR and HRV datasets.EUMETSAT Meteorological Satellite Conference, 3-7 September 2012, Sopot, Poland 13

Relations between Minimum OT BT, Anvil BT and OT HeightPart 1

Colder anvils produce also colder OTs.An average OT is 8 degrees colder then anvil.If dry adiabatic lapse rate is 9.8 K/km,averaged OT height = 8[K]/9.8[K/km]=816m.

The average OT height based on shadowsis 1800m, which is due to missing measurementsfor OT height < 1000m and subjective preferenceof higher OT cases.

Averagedheight = 1800m

Missing measurements for OT < 1000m due to limitation of manual shadow technique

Dry adiabatic lapse rate = 9.8 K/km(http://en.wikipedia.org/wiki/Lapse_rate)


http://en.wikipedia.org/wiki/Lapse_rate



Gain the Height dependence on both Min OT and Anvil BTs:

Colder anvil produce colder and higher OTaccording lapse rate, which can beexpressed by polynomial formbut with strong bias from adiabatic theory(9.8K/km)


y = -0,0118x2 + 0,4695x + 2,5899


Dependence of OT height(BT diff) distributionon Sun elevation angle:


Sun elevation angle

The sequence of data filtered on Sun elevation to show how to obtainthe most realistic dataset of co-located OTs. Finally all detections withSun elevation higher then 15° were removed from processing.


Error in OT-anvil IR BT difference resulting from low image resolutionCourtesy of Scott Bachmeier (UW-CIMSS)

Resolution of left image (~5.5 km GOES) is lower then right (~1 km MODIS), therefore the fine structures and also extremely low BT spots are not resolved in the GOES image.

It is the reason why Minimum OT BT can be higher then real value, mainly whenhorizontal size of OT is lower then pixel resolution. Recent studies have found a warm bias of ~9 K when OTs are examined in in geostationary vs. polar orbiting data

Adding the 9 K bias to the OT-anvil BT vs HRV penetration height analysis would yield a lapse rate of 9.1 to 9.4 K/km that is much more realistic


Part 3

Sources of errors resulting from shadows location sun elevation anvil layer height and parallax shift low image resolution

Examples of correct and failed cases


Sources of errors in OT penetration height estimation by HRV method• Error of visual location of shadow start at OT center (shadow head) (error

is substantial)– OT center is not touching the shadow directly, set subjectively, no IR data used

• Error of visual location of shadow end on the anvil (shadow tail) (error is substantial)– Shadow of one OT can be broken by another OT– Shadow tail is located out of anvil (out of projection area)– Anvil is too transparent or is not a plain– Observed cloud structures are not regular OTs and/or cirrus clouds

• Error resulting from Sun elevation (error is substantial)– Too long shadows when elevation is very low (<2°), shadow beyond terminator– Too short shadows when elevation is very high (>25°)

• Error resulting from parallax shift (error is negligible)– Correction implemented in the tool; necessity of manual adjustment– In some cases it is hard to allocate proper cloud structure to the shadow

• Error resulting from absolute anvil layer height above Earth surface– Error can be compensated visually (error is negligible)

Some mentioned errors are relative to native MSG HRV image resolution and some relate to subjective evaluation by observer.


This error includes uncertainty in location of shadow head and shadow tail with assumption that shadow can be localized within the precision of MSG native resolution.Valid for central European region.

Error resulting from Sun elevation

This plot was created using algorithm implemented in ViewMSGProc measurement tool.


Error resulting from anvil layer height and parallax shift

Because parallax shift is too small in comparisonto shadow length in prevalent number of cases,error resulting from parallax shift is negligible.In extreme case it is source of overestimationof shadow length. Can be corrected subjectively.

Because anvil layer height is too small in comparisonto Earth radius, error resulting from anvil layer heightis negligible. Nevertheless correction of this error isimplemented in measuring tool.

Para

llax

shift

Real shadow length

Measured shadow length


Anvil layer

Sea level

4.8.2009 07:15 UTC; Sun elevation: 38,5°; Estimated height: 5793m

Comment: Sun elevation is very high; Error of estimation is around 3000m.

FailedEstimation error: 2900m

Example 1



Comment: Shadow of OT is interrupted by another cloud structure.


Example 2



Comment: Shadow of OT is interrupted by another cloud structure.


Example 3



Comment: Structure of anvil is not a plain. Height of Cb over mid-level clouds was measuredinstead of OT.


Example 4



Comment: Well depicted OT with shadow. Length of shadow well measureable.

SuccessEstimation error: 250m

Example 5



Comment: Quite well depicted OT with shadow. Length of shadow well measureable.


Example 6



Comment: OT shadow is measureable, case was added into processed data set.


Example 7



Comment: Well depicted OT with shadow. Length of shadow well measureable.


Example 8


ConclusionsWe used significant dataset of OTs, more then 20000 detections from IR and 5000 from HRV SEVIRI imagery with the aim to show whether OT penetration height is measurable by shadow technique and if some relations between IR BT and height parameters can be found.

We identified some problems of shadow technique and their influence on the results. Thereafter we processed datasets to show relations. Only subset of IR and HRV data were coupled – co-located successfully. Main reasons are limited resources of manual shadow technique and different IR and HRV channels resolution.

The trend line between OT penetration height and Min OT BT and Anvil BT difference we derived possible lapse rate of OT cooling above the anvil level from 3 to 7.3K/km, which is too low in comparison to generally valid value of 9.8K/km

We assume that two factors – low IR resolution of SEVIRI imager and inability to detect very low OTs by shadow technique are the reasons of bias for lapse rate derived from statistical data set.

We assume that comparison of time matched SEVIRI and early evening NOAA-17 AVHRR HRPT observations over Europe could help quantitatively show the bias and its relation to OT height

This work was based only on 15-minute SEVIRI observations and it is not enough to estimate at what point in OT lifecycle was sampled. Rapid scan imagery with higher frequency (< 2.5 min from SEVIRI, 30 seconds from GOES-R ABI) can help to show where IR-Height relation for OTs starts to break down from adiabatic theory as OTs warm via mixing with the stratospheric air


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OVERSHOOTING CONVECTIVE CLOUD TOP HEIGHT ANALYSIS OVER CENTRAL EUROPE , SUMMERS 2009 - 2011

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