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Validating the AVHRR Cloud Top Temperature and Height product

using weather radar data

COST 722 Expert Meeting 9.-11. 6. 2004

Sauli Joro

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•Introduction

•Weather radar & TOPS product

•Cloud Top Temperature and Height product

•Data

•Product comparison

•Results

•Summary

Outline

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•Visiting Scientist Activity within EUMETSAT SAFNWC framework

•EUMETSAT Satellite Application Facilities (SAFs) are programs specialized in developing and processing satellite data

•SAFNWC is consentrated on nowcasting and very short range forecasting

Introduction

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•The Cloud Top Temperature and Height (CTTH) product is developed within the SAFNWC framework

•Objective is to validate CTTH product with weather radar data – known to be very ambitious

•Study mainly concentrated on opaque and semi-transparent high clouds (clouds containing ice)

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Weather radar & TOPS product

•”Direct” cloud measurement with good temporal and spatial resolution

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Experimental ice particle distributions in different

cirrus clouds (Liou, 1992).

water: 0.93 ice: 0.176

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•”What is the threshold value for an echo in dBZ to be considered as the cloud top?”

•FMI & Finnish Air Force: ”in case of ’raining cloud’ treshold value of –10 dBZ should give reliable cloud top heights”

•Poutiainen (1999): ”Various cirrus types can create Z values between –15 - +5 dBZ

•-5 dBZ and -10 dBZ selected to represent the true tops of ice clouds

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Thick cirrus on RHI screen taken from Radar Vantaa on 25 June 1998 at 10:59 UTC (Poutiainen,

1999)

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Thin cirrus on RHI screen taken from Radar Vantaa on 22 July 1998 at 07:29 UTC (Poutiainen, 1999)

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•Uses 3-D weather radar data

•Vertical resolution 100 m

TOPS product

•For each pixel downward search in cylindrical coordinates at constant range for the chosen dBZ treshold value and determines if it is crossed -> interpolation

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TOPS underestimation

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CTTH product•Aims providing reliable estimates of the cloud top temperature and height for all the cloudy pixels within an AVHRR scene

•Vertical resolution 200 m

•Consists of two algorithms: one for opaque and one for semi-transparent cloudiness

•Results of Cloud Mask and Cloud Type products used as input data

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CTTH opaque retrieval

•Applied to all pixels classified as ’opaque’ by the Cloud Type product

•Based on Radiative Transfer Model simulations and results of NWP model

•For each pressure level the RTM simulates the AVHRR channel 4 cloudy brightness temperature -> the best fit to the measurement is selected -> CTH from NWP

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CTTH semi-transparent retrieval•Measurement itself is

”contaminated” as the radiation from the surface is partly passed through the cloud -> brightness temperatures too warm

•Method uses AVHRR channels 4 and 5 brightness temperatures

•Result is applied to all pixels within an image segment classified as semi-transparent

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Example distribution of 32x32 pixel size image

segment.

semi-transparent

opaque

clear

•Single cloud layer

•Constant absorption

coefficient throughout the

cloud layer

•Brightness temperature

depends linearly on radiance

•No atmospheric absorption

•Local thermodynamic

equilibriumthermodynamic cloud top temperature

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Data

•Selected time period April and May 2003

•301 satellite overpasses

•FMI radar data, 15min temporal resolution

•NWP used: HIRLAM

•RTM used: RTTOV

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•Maximum elevation angle sets a certain minimum distance for cloud top height detection

•Radar sensitivity sets a maximum distance for each dBZ value

”Donuts” around radar sites

•Proper –10 dBZ cases only from Radar Utajärvi

•Cases divided into two sub-categories -5 dBZ and -10 dBZ

Radar data features

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Product comparison

•Cases selected subjectively from AVHRR imagery together with Cloud Type and CTTH product outputs

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•Simple comparison method used – data may not be normally distributed

•Pixel-by-pixel comparison not reasonable -> data needs to be averaged

•Data is classified to histogram with 200 m class intervals -> resolution difference vanishes

•Modes are taken as the results of the products

•Mode difference = TOPS – CTTH

•Comparison is considered to be successful if the absolute value of mode difference is less than 500 m

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Results

TOPS-CTTH

both -5 dBZ and -10 dBZ cases

41% success

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TOPS-CTTH

radar echoes below 2000m discarded-10 dBZ applied to all cases

57% success

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TOPS-CTTH

no discarded echoes

cases within r-10 coverage area

75% success

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Case A: Opaque high cloud from Radar Ikaalinen -5 dBZ coverage. Satellite over pass is received on 1

May 2003 at 10:43 UTC.

timing -2 minutes

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Case A frequency distributions and HP’s.

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Case B: Opaque high cloud from Radar Anjalankoski -5 dBZ coverage. Satellite over pass is received on 29

April 2003 at 19:15 UTC.

timing -3 minutes

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Case B frequency distributions and HP’s.

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TOPS-CTTH

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Case C: Semi-transparent high cloud from Radar Utajärvi -10 dBZ coverage. Satellite over pass is

received on 13 May 2003 at 10:47 UTC.

timing -6 minutes

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Case C frequency distributions and HP’s.

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Summary

•A new approach for the CTTH product validation presented

•The approach presented here uses 3D radar data

•Result interpretation very challenging as both methods are based on numerous assumptions – lots of different error sources

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•Results on the opaque high cloud categoty promising – more than 55% of the comparisons successful

• Still lots of unsuccessful comparisons with no explicit explanation, also Cloud Type misclassifications occurred

•-10 dBZ proved to be a good first guess for the top heights of thick ice clouds

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•Results on semi-transparent high cloud category disappointing with only three successfull comparisons

•-10 dBZ threshold turned out to be too high for the semi-transparent high clouds and more sensitive values should be applied in the future

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A weather radar is not the best possible tool for the CTH validation, however, it can offer valuable information about the cloud tops in various situations being at its best when the cloud consists lots of ice particles.

The lack of 45-degree elevation angles limited proper -10 dBZ cases to Radar Utajärvi

Conclusion

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Report available at

http://www.smhi.se/saf