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Poster Session R
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Estimation of cloud type, its amount and precipitation area using NOAA – AVHRR data
M. Bayasgalan, M. Erdenetuya
National Remote Sensing Centre, Mongolia Ulaanbaatar – 11
Abstract
NOAA-AVHRR digital gave a possibility to estimate cloud types, amounts and precipitation area over continuous large space within short time and with less expenditure, based on digital image data processing methods of thresholding, multivariate statistical analyzing and supervised classification.
The results of this work are used for environment monitoring, especially in field of weather forecasting, expansion of the ground observation data range in terms of time and space.
Introduction
One of the main factor, affected on natural environment state is the climate, that is determined by dynamically changeable weather. Therefore the system intended for natural environment monitoring should include weather diagnosis analysis and forecasting components.
The weather characteristics, especially the cloud coverage and precipitation area are very changeable and the ground observation discrete data from few meteorological stations are not enough for their full estimation.
The remotely sensed data from meteorological satellite NOAA can provide very important and useful information in meteorology, mainly in field of weather forecasting. The satellite data is much significant for these purposes, due to its continuity, repetition and wide coverage area of observation.
Since, 1988 we are receiving and processing multi channel AVHRR data from NOAA satellite and developing some methods to estimate cloud coverage and to detect precipitation area using the radiance temperature, spectral albedo and some cloud statistical parameters.
In this paper we have briefly introduced the methods and some results of our work.
Data and Methodology
We have used NOAA-AVHRR and ground meteorological observation data over territory of Mongolia. We have developed methods for estimation of cloud amounts, cloud types and delineation of precipitation area.
1 Cloud amounts
The recognition of cloudness is most important task in its estimation. It can realized using following methods:
- Threshold method : It is used the radiance temperature of channel 4 (T4) and spectral albedo of channel 2(A2).
- Discriminant analysis method : In this method for cloud recognition, the ground observation data has been selected as source data and based n discriminant function there is extracted cloudness over those territory where ground measurement data is absent or rare.
- Supervised classification : In this method the training area was selected interactively or such area where has ground observation cloud data.
After recognition of cloud we calculated its amounts using following simple expressing as ratio of cloudy and non-cloudy pixel numbers:
N = P / Pn * 10 (1)
Where,
P is a number of cloud pixel (one pixel has 1.1 sq. km resolution)
Pn is a square of an area, where calculated cloud amount (40 sq. km)
2 Cloud types
Different kind of clouds are expressed with their own characteristics in each channel of AVHRR data.
According to many properties of cloudness have been interactively determined cloud type and picked out their corresponding radiance temperature of channel 3 and 4 (T3, T4), spectral albedo of channel 2(A2) and their deviations (Sigma).
3 Precipitation area M
In this work we have used the cloud top radiance temperature, derived from AVHRR data for 3 and 4 channels and also cloud types and amounts as predictors.
Result and Discussions
1 Cloud Amounts
- Threshold method : Based on this method have been selected the threshold values of -30°C, in winter season and -5°C in Summer season for radiance temperature of channel 4 (T4). Threshold values for spectral albedo of channel 2 (A2) are 30 for winter and 20 for summer.
- Discriminant analysis method : In this method we have selected T3 and T4 as bets predictors and used following discriminant function :
D cl = 0.054 * T3 – 0.174 * T4 – 1.46 (2)
Where, T3 and T4 are values of radiance temperature for channel 3 and 4
- Supervised classification method : This method was more suitable when it is impossible to use threshold and discriminant methods for estimation cloud especially for winter time, when differences between cloud top temperature and cold surface temperature became less, even when cloud became that landcover.
The final result shows that 83.8% of case-study for cloud amount had differences of 1 - 2 value between calculated and observed parameters and 8% of its has 5 value of differences. The main errors are observed in case of micro-scale cumulus (Cu) cloud.
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