Datasets and study methodology

a) Dataset description

The Meteosat-7 datasets used in this work are: infrared brightness temperature in Kelvin TIR(K), infrared water vapour band in Kelvin TWV(K) and the visible channel in

brightness counts. The Meteosat datasets, provided by the INM, were produced by EUMETSAT in images every 30 minutes with a pixel spatial resolution over Albania around 7 by 7 km lat-lon. All satellite rainfall processing shown in the present chapter is

performed on this spatial resolution. They were resized to 300 lines by 300 columns and centred in 40ºN 12.4ºE. The period of the satellite dataset used in this study was from 0000 UTC September 21 to 2330 UTC September 23.

Ground rainfall rates in mm h-1 recorded every 30 minutes from 0100 to 2330 UTC by 8 stations during the three days of the flood case (see black boxes in figure 5.1). On September 21st only 6 stations of these have provided data, while the next day all the 8 stations were working properly but on September 23 the data of only 5 stations could be employed for this research. These rain rates were used for the infrared curve calibration process as described in section 5.4.

Rainfall accumulations in 24 hour period obtained between 0000 to 2400 UTC from September 21st to the 23rd by 115 stations in Albania. However only 81 were finally used after a supervised quality check (see white and black boxes in figure 5.1). These daily datasets were used for the verification of algorithms and the sensitivity test of rainfall corrections.

Grid analyses from the ECMWF are used to run the MM5 simulation. They have a 0.3º by 0.3º lat-lon resolution and are available every 6 hours from 0000 UTC September 21st to 0000 September 24th.

b) Study methodology

In situ daily rainfall measurements from the 81 stations are interpolated by a “kriging” analysis method using the linear model for the variogram fit. This minimal error variance method is recommended for irregular grids with relatively low number of observations (<250) and has been widely used to compute rainfall fields from rain gauges (Krajewski 1987, Seo 1998, Bhagarva and Danard 1994). In the Albanian region as in many others, mountain areas are not well covered by the meteorological stations therefore daily precipitation may be under-estimated in these mountainous places. After careful supervision of the data analysis process, the resulting kriged daily rainfall fields obtained from the rain gauge measurements were, in our opinion, correctly delineated over these complex terrain areas. The interpolated rainfall fields are then remapped to a satellite projection and resolution for the three days of the flood case in order to facilitate visual and numerical comparisons with respect to the MM5 and satellite estimations.

In previous studies the verification has been performed using coarse grids of 0.25ºx0.25 or 0.50ºx0.50º or 1ºx1º resolution in an attempt to minimize the high spatial variability of rain rates and hourly precipitation measurements (Marrocu et al. 1993; Oh

et al. 2002; Vicente et al. 1998). In the present research the “kriging” option has been selected because it allows a pixel by pixel comparison which is suitable for studies in small areas such as the Albanian territory. Other important reasons are: firstly, daily measurements are much less sensitive than rain rates and/or hourly fields to the spatial and temporal variations of the precipitation and, secondly, the Albanian rain gauge network (figure 5.1) is dense enough to capture the most important daily rainfall patterns in such a severe event.

The A-E computes rainfall rates based on a fixed, non-linear, power-law relationship as described in chapter 3. However before the algorithm was applied, cirrus cloud pixels with a low probability of rain, are filtered using the empirical slope test developed by Adler and Negri (1988). A qualitative study not shown in this report has illustrated to us that this simple process applied to infra-red images can detect cold pixels from homogeneous cloud top surfaces suspected of being non precipitating cirrus. These pixels are observed mostly in clouds that are moving but not growing and developing as occurred to convective. The method is simple, for each point Po an empirical slope S and a kind of temperature gradient Gt are computed in a window of 25 pixels centred in the point Po. The terms Gt and S are given by the following equations:

Gt = Tavg - Tmin (5.1)

S= 0.568(Tmin – 217) (5.2)

where Tmin is the local minimum and Tavg is the average temperature in the grid of 5 by 5

pixels. A large Gt indicates convective clouds, a small Gt a weak gradient associated with cirrus clouds within the window. Pixels having Gt less than S are classified as cirrus clouds and therefore, considered as non-precipitating points with 0 mm h-1.

Concerning the CRR algorithm; the 2-D and 3-D matrices developed over the Iberian Peninsula and shown as a table 4.3d and 4.5 respectively are used in the Albanian flood study. We are interested in a CRR algorithm evaluation in a place different from the one where matrices were tuned. However, both regions, the Iberian Peninsula and Albania, are located on similar latitudes in Europe. Visible counts from Meteosat images have to be normalized using the solar zenith angle (Binder, 1988) before the 3-D matrix is used to estimate the rain rate in a point. To avoid problems during the transition from night to day and from day to night, the use of the 3-D matrix

has been limited from 0700 to 1500 UTC (daytime in Albania in September). The rest of the time has been employed by the 2-D matrix which only needs brightness temperatures from the two infrared bands.

Satellite rainfall images estimated from the different methods are rain rate fields in mm h-1 units. Daily precipitation images are then computed using equation 5.3. It represents the numerical integration of rain rates each 30 minutes throughout a 24 hour period.

(5.3)

where DP(x,y) is the daily rainfall at image coordinates (x, y) and IPt(x, y) is the rain rate

every 30 minutes at that point.

MM5 accumulated rainfall in 24 hours is also remapped for each of the three days in order to test the accuracy of the numerical model simulation (see MM5 sets in the next subsection). Observed and estimated daily rainfall fields from satellite and MM5 are compared in a qualitative and quantitative manner. For the quantitative verification, common statistical indices such as the difference between the estimated and the observed spatial averaged daily precipitation (BIAS), root mean square errors (RMS) and Pearson correlation coefficient (CORR) are calculated in the area limited by kriging analysis derived from the rain gauges.

As illustrated in section 5.3, results show that A-E over-estimates strongly the daily precipitation and therefore the infrared power law curve was adjusted for the specific conditions by an experimental calibration process as described in section 5.4. Finally, A-E, CRR and calibrated A-E rainfall were corrected by moisture, cloud growth rate, cloud top temperature gradient, parallax and orographic and a sensitivity study was carried out. ∑ = ⋅ = 48 1 5 0 t . (x, y) IP DP(x, y) _t

0 25 50 km

Figure 5.1. Albanian terrain and meteorological stations. White boxes represent daily rainfall measurements and black boxes daily measurements and rain rates. The territory is very irregular in general, but the highest mountains, above 2300 m, are found in the north and in the south of the country.