RAIN-RATE ESTIMATION FROM SEVIRI/MSG AND AMSR-E/AQUA. VALIDATION AND COMPARISON BY USING U.K. WEATHER RADARS

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1 RAIN-RATE ESTIMATION FROM SEVIRI/MSG AND AMSR-E/AQUA. VALIDATION AND COMPARISON BY USING U.K. WEATHER RADARS Capacci Davide 1, Federico Porcù 1 and Franco Prodi 1,2 1. University of Ferrara, Department of Physics, via Saragat 1, Ferrara, IT 2. ISAC-CNR, via Gobetti 101, Bologna, IT ABSTRACT The satellite rainfall estimation algorithm here proposed is based on a statistical approach (Artificial Neural Network: ANN): it needs only radiation satellite measurements as input data and provides, at 5 km of spatial resolution, surface rain-rate classification onto five classes of precipitation: [less than 1/32] mm/h (no rain), [1/32, 0.125] mm/h (slight rain), [0.125, 0.5] mm/h (slight/moderate rain), [0.5, 2.0] mm/h (moderate), [more than 2.0] (heavy rain). The algorithm works for U.K. area, daytime and summer season and adopts a cascade method where at first a rain no-rain classification is computed and then a similar yes-no classification is computed for the other pair of classes. It has been developed and validated with the use of U.K. weather radar rainfall estimates for both SEVIRI (on the geosynchronous Meteosat-8 satellite) and AMSR-E (on the low earth orbit AQUA satellite) sensors. To assess the performance against radar rainfall estimation some skill indicators are computed: the Equitable Threat Score (ETS) and BIAS are used for pair of classes of precipitation whereas the Heidke Skill Score (HSS) is used for the four raining classes. The validation procedure (over U.K. area and for June, July and August 2004, at noon time), shown that the nine channels SEVIRI classifier provides performances very close to the ones provided by the twelve channels AMSR-E classifier. The advantage of using AMSR-E measurements is more evident when only sea area is considered. The analysis also show which are the best sets of channels (among the nine SEVIRI channels and the twelve AMSR-E channels) that give the most important contribute to the above performances. 1. INTRODUCTION The precipitation estimates from satellite are an important challenge in meteorological remote sensing and contribute to several meteorological activity as Nowcasting system, forecasts verification, regional hydrological monitoring and climatological and water cycle studies. Active and passive sensors on board satellites are used to cope with those issues. Relatively to passive sensors, important satellite radiation measurements occur for the visible/infrared (VIS/IR) spectrum and for the microwave (MW) spectrum. The advantage of using VIS/IR comes from the possibility to get those measurements with high spatial resolution and, when the sensor is on board geostationary platform with high temporal resolution. The disadvantage is that VIS/IR radiation does not directly carry information about the precipitation layers but only about the top of the cloud. On the contrary the direct interaction with precipitating layers is the advantage of using MW. Unfortunately, at the moment, that radiation can be measured in a reasonable spatial resolution only if the sensor is on board polar satellite with the consequence of also a poor temporal resolution. To exploit at best the characteristics of VIS/IR and MW satellite sensors, deep interest has been recently focused on methods and algorithms that merge and combine the information from both the two kinds of measurements. In particular the direct precipitation estimates from MW are enhanced by the use of IR geostationary measurements in term of spatial and temporal resolution. Examples are

2 the C-morph (Joyce et al., 2004) and IR-MW blended (Turk et al. 1999) techniques. Those procedures, however, do not properly consider the real potential of new geostationary channels as the 3.9 µm and 1.6 µm. Moreover, the real difference between MW-based and VIS/IR-based precipitation estimates have not yet been well established. This work means, therefore, to contribute to the following point that come out from the above discussion: investigating the difference between VIS/IR and MW precipitation estimation performances on view their proper use in a merging/combining technique. 2. DATA SET DESCRIPTION For this work, VIS/IR and MW satellite measures come respectively from the Spinning Enhanced VIsible and InfraRed Imager (SEVIRI: on board the geostationary Meteosat-8 satellite) and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E: on the polar-orbiting AQUA satellite) whereas the surface rain rate truth maps are provided by the radar composite from the U.K. Met Office Nimrod system (Golding, 1998). The SEVIRI is able to scan the earth surface every 15 minutes, with a sampling of 3 km at the subsatellite point. The upwelling radiation is measured at 12 channels from which the nine here considered to estimate precipitation are centred on the following wavelengths: 0.6µm (VIS0.6), 0.8µm (VIS0.8), 1.6µm (NIR1.6), 3.9µm (IR3.9), 6.2µm (WV6.2), 7.3µm (WV7.3), 8.7µm (IR8.7), 10.8µm (IR10.8), 12.0µm (IR12.0). The data have been freely obtained from the EUMETSAT Archive Service. AMSR-E measures the upwelling scene brightness temperature at 6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz (horizontally and vertically polarized). The spatial resolution ranges from 5.4 km at 89.0 GHz to 56 km at 6.9 GHz. The AMSR-E/AQUA L2A Global Swath Spatially-Resampled Brightness Temperatures (Ashcroft and Wentz, 2003) are freely available from the online Data Pool at the NSIDC Distributed Active Archive Center (DAAC). Radar data are collected, at 5-minutes interval (nominally starting at 00 each hour), from a network of 15 C-band (5.3 cm wavelength) radars across the UK (Fair et al, 1990). The radar derived precipitation rate product represents average instantaneous rain rate over a pixel area (5 km x 5 km) and the smallest nonzero rate is 1/32 mm h -1. Analysis of the representativeness errors have been carried out by using rain gauges and the Root Mean Square Factor (RMSF) error results approximately a factor of two in the first 100 km range (Harrison et al 2000). Outside this range the error is larger and therefore only the first 100 km of the radar range is here considered. The rain rate estimate from radar is a continuous variable, however, as an indirect precipitation estimation algorithm for satellite measurements is pursued, it is convenient to consider classes of precipitation. The classes considered are the five ones used into the Nimrod system and represent a proper choice so as to have, for each class, an enough number of pixels for significant statistical analysis. They are: class 0: [0, 1/32] mm/h, class 1: [1/32, 0.125] mm/h, class 2: [0.125, 0.5] mm/h, class 3: [0.5, 2.0] mm/h, class 4: [more than 2.0] mm/h. By considering the AQUA overpasses over the U.K. area, at time between 12 and 14 UTC, 92 AMSR-E images are collected for June, July and August The corresponding 92 SEVIRI images, with a maximum temporal lag of 11.5 minutes, have been collected. SEVIRI and AMSR-E images have been re-mapped onto the radar grid at 5km of spatial resolution. For both the series of AMSR-E and SEVIRI images the corresponding radar images have been collected. The maximum temporal lag between SEVIRI and radar pixels is around 4.75 minutes whereas for AMSR-E and radar pixels is around 6.5 minutes. 3. SATELLITE ALGORITHM DESCRIPTION: BUILDING PHASE The indirect relationship between VIS/IR cloud-top radiation and ground precipitation suggests the definition of the SEVIRI rain classifier by means of a statistical approach. In this work the AMSR-E rain classifier is built by considering a statistical approach as well. Artificial Neural Networks are the statistical tool chosen to define the statistical correlations between satellite measurements and classes of ground precipitation as estimated by weather radars. The satellite rain classifier scheme here adopted needs four ANNs used in cascade, as illustrated in figure 1. Starting from a satellite image, the first ANN is trained to separate the class 0 pixels (dry pixels) from the other rain classes of pixels (wet pixels). The second ANN is applied to all the wet pixels and extracts from them the class 1 pixels. The third ANN separates the class 2 pixels from the class 3 and class 4 classes and, at the end, the remaining pixels are classified into the class 3 or class 4 by means the fourth ANN. Each Neural Network used in that scheme is a Multilayer Perceptron or MLP (Rosenblatt, 1962), which consists of input nodes, hidden nodes and one output node, the nodes being called perceptrons.

3 To build the four ANNs, four ensembles of supervised pixels (called building ensembles ) have to be prepared. They are gathered from a group of supervised cases. Each building ensemble have to contain pixels from the two precipitation classes (or sum of classes) that the corresponding ANN have to learn to recognise. Those pixels, on depending upon their belonging to the two classes as defined by means of radar information, are labelled with 0 or 1. From each building ensemble, by a random splitting procedure, the training and testing ensembles are obtained, the first is used to train the ANN and the second to test it. This procedure is repeated for the four ensembles and, at the end, the four ANNs are determined. The satellite classification scheme, obtained with the four ANNs, is therefore ready to be validated over a data set independent from the building ensemble. The ANN configuration used is the single hidden layer version employing a sigmoidal transfer function at the nodes. The input nodes are the numerical values from satellite channels: all the available channels or a proper selected set can be used. The output node provides a number between 0 and 1 that, with a threshold value, may be applied to distinguish the two classes of precipitation under consideration. Details on the ANN architecture and on the ANN building procedures here adopted can be found in the Pankiewicz et al. (2001) and Capacci and Conway (2005). Figure 1. Rain rate classification scheme: cascade method. Four ANNs are needed to carry out the cascade classification into five classes of precipitation from multi-spectral satellite data inputs. 4. THE VALIDATION PROCEDURE The first step of our validation procedure is to select a number N of satellite cases and N corresponding radar precipitation maps. They are called validating cases and have to be independent from the building cases. As the cases here considered come from different days, by randomly splitting them in two groups their independence is guaranteed. The first group is used to built the ANN (training and testing) and the second one to validate it. Then, reversely, the second group is used to built the ANN and the first one to validate it. In this way all the available cases enter into the validation. The final validation result will be therefore expressed as the mean value between the two validations. Once that supervised validation data set is prepared an essential description of its characteristics should be provided as in table 1 for the used precipitating validation data set. As second step the N radar observed and satellite predicted maps have to be quantitatively compared. The comparison is carried out by using some skill indicators as the ETS (Equitable Threat Score) and BIAS for two classes of precipitation and the HSS (Heidke Skill Score) for the four precipitating classes. For the latter the no-rain pixels are not considered. The advantage of this choice is that the validation for the only raining pixels is not affected by the rain no rain classification that is easier and, due to the large number of no raining pixels, would have an impact to much important in the corresponding statistical parameters computed. Table 1. [12-14] UTC summer 2004 data set description

4 The way to determine the contingency tables defines two validation procedures: 1. ensemble validation- By collecting all the pixels from the N observed and predicted rain classified maps the validating ensemble is built. A corresponding contingency table is then defined and the required statistical parameter is computed. 2. cases validation-from the N observed and predicted rain classified maps N contingency tables are built. For each table the required statistical parameter is computed. The obtained N computed values are then averaged in order to express the required statistical parameter as a mean value among the N cases with associated the computed standard deviation. Differentially from the validating ensemble the use of validating cases implies the taking in account of precipitation features variability. In addition, for several studies and operational aims, the wet events represent the interesting situation for the which it is useful to know how the satellite rain classifier works. The validation procedure is then applied to the total validation data set and, in a second time, to a wet data set. The latter is determined by selecting cases with a DWR less than VALIDATION RESULTS The validation scheme above described is applied for both the SEVIRI and AMSR-E classifiers. The results are shown in table 2 and make possible the direct comparison between performances obtained from the SEVIRI and AMSR-E sensors. Table 2. Validation results and comparison between SEVIRI and AMSR-E performance. Parenthesis contain the corresponding standard deviation. The main result pointed out from table 2 is that the two sensors perform very closely. To better interpret this statement, in contrast with several results in literature, the following further considerations have to be considered: - the used AMSR-E images are not parallax corrected and are produced from original data at low spatial resolution, - the mean rainfall rate of the data set in exam is around 1 mm h -1 whereas it is known that MW precipitation retrieval works at best in higher rainfall rate condition, - the proper use of MW for precipitation retrieval is over the sea The latter point has been proved by separately carrying out the validation over the three type of background: sea, land and coast. This is reported in figure 2 and 3. Figure 2. Validation results (ETS) for the rain-no rain classification over sea, land and coast ground terrains.

5 Figure 3. Validation results (HSS) for the classification into four raining classes over sea, land and coast ground terrains. The figures 4 and 5, here after reported, shown two examples of the rain classification as obtained by the radar network and by the two SEVIRI and AMSR-E algorithms. In those example the HSS values have been computed by considering all the five classes of precipitation (included the no rain class). Figure 4. Case , UTC. Radar, SEVIRI and AMSR-E maps are respectively on the left, in the middle and on the right. Figure 5. Case , UTC. Radar, SEVIRI and AMSR-E maps are respectively on the left, in the middle and on the right.

6 6. CHANNEL ANALYSIS Though all the channels (nine for SEVIRI and twelve for AMSR-E) are used as inputs for the final ANN it is important to know which channels provide the most important contribute for the rain rate classification. This is particularly useful for simplified satellite rain rate estimation scheme for which a limited set of input data is required (Francis et al, 2006) or to address deep physical study (Capacci et al. 2000, Battaglia et al, 2000).This analysis has been carried out in the frame of the building phase and the ratio between number of pixels belonging to the two different classes is always fixed to 1. The channels analysis is carried out by considering any combination of channels as ANN inputs with in addition the solar zenith angle variable. If K is the number of ANN inputs and N the possible channels being considered as inputs ( N=9 for SEVIRI, N=12 for AMSR-E), the number of their combinations is: N!/(K!(N-K)!). The number K varies from one to the N and the total number of combination is therefore 2 N -1. This means 511 combinations for SEVIRI and 4095 for AMSR-E. The analysis is carried out for each of the four ANNs that enters the rain rate classification scheme. Therefore, for each channel combination its skill to classify pixels in the four pair of classes (class_0/class_( ), class_1/class_(2+3+4), class_2/class_(3+4), class_3/class_4) is evaluated by computing the ETS parameter. The most significant results for SEVIRI and AMSR-E channels are reported here after. 1) SEVIRI The following figure 6 illustrates how the SEVIRI rain classifier performances increase by increasing the number of input channels. Figure 6. ETS results by increasing the number of input SEVIRI channels. It is interesting to explore which are the pairs of channels that have the best skill to separate each pair of classes or sum of classes. SUMMER 2004 VIS0.8,NIR % VIS0.8,NIR % VIS0.6,NIR % VIS0.6,NIR % VIS0.6,IR % VIS0.6,IR % VIS0.8,IR % VIS0.8,IR % VIS0.8,NIR % VIS0.8,NIR % VIS0.6,NIR % VIS0.8,IR % VIS0.8,IR % VIS0.6,NIR % VIS0.6,IR % VIS0.6,IR % SUMMER 2004 VIS0.6,VIS0.8 VIS0.6,NIR % 22.0% NIR1.6,IR3.9 IR3.9,IR10.8 VIS0.8,NIR1.6 VIS0.6,VIS % 19.5% IR3.9,IR12.0 NIR1.6,WV7.3 Table 3. Best four results from the two-channels analysis. Table 4. Best results from the four-channels analysis That analysis is reported in table 3 and shows that always the best pairs of channels are: VIS08/NIR1.6 and VIS06/NIR1.6. Close performances are provided also combining a visible channel with IR3.9. These facts are an important confirmation of the several study and results already available in literature (King et al.1997, Rosenfeld et al and 2003, Capacci et al. 2004, ) about the top-cloud microphysical information hidden into the NIR1.6 and IR3.9 measurements and that can be exploited to better infer ground precipitation.

7 In the plot in figure 6, the yellow vertical line indicates that performances quite close to the maximum performance are obtainable by considering only four channels as ANN input. The analysis reported in table 4 illustrates which should be those four channels for the four ANNs. 2) AMSR-E For MW the analysis has to be carried out separately for sea, land and coast. The figure 7 illustrates how the AMSR-E rain classifier performances increase by increasing the number of input channels. As not particularly novelties are obtained from the two-channels analysis only the four channels analysis is reported in table 5. Figure 7. ETS results as obtained by increasing the number of input AMSR-E channels. The analysis has been done separately for sea (continued line), land (dotted line) and coast (dashed line) areas. SUMMER 2004: SEA 10.7V,36.5V 23.8V,36.5V 67.2% 31.2% 18.7H,23.8H 10.7H,18.7H 32.6% 31.8% 36.5V,89.0V 23.8H,89.0V SUMMER 2004: LAND 23.8V,36.5H 6.9H, 10.7H 52.7% 23.0% 36.5V,89.0V 23.8V,89.0V 18.7V,23.8H 6.9V, 18.7V 22.3% 20.7% 36.5V,89.0H 23.8V,36.5V SUMMER 2004: LAND 10.7H,18.7V 6.9V, 23.8H 54.7% 26.3% 23.8H,36.5H 6.9V, 10.7H 25.3% 17.1% 36.5V,89.0V Table 5. Best results from the four-channels analysis over sea, land and coast Among the sets of four channels proposed in table 5 the channels with more occurrences are 89.0GHz V/H and the 36.5GHz V/H. Low frequency channels (6.9GHz, 10.7GHz and 18.7GHz) become important for high rain rate classes as well know from literature. 7. CONCLUSION The main aim of the work was to investigate the difference between VIS/IR and MW precipitation retrievals. A secondary aim was to set up a validation procedure useful for addressing the above point and also to simplify, for the reader, the understanding of the statistical parameters meaning. Concerning the validation procedure the main point stressed by the work is the importance to describe the validation data set used and the way to compute and express the statistical parameters as the BIAS, ETS and HSS. About the first point the analysis shows that SEVIRI and AMSR-E sensors over the summer specified data set perform in a very close way. This is quite on contrast to some indication from literature on favour the MW precipitation retrieval. Therefore, as further work, the same analysis should be enlarged to other geographical areas, to other sensors, to other seasons and specially to night time for which better performance are expected from MW. This might help to better understand and define the way to merge the precipitation information from VIS/IR and MW satellite sensors. The analysis over different type of ground terrain confirm that the AMSR-E perform better the SEVIRI sensor if only sea ground terrain is selected. Finally the channel analysis confirm that for the SEVIRI classifier the most important contribute come from the VIS/1.6 µm combination. Acknowledgements The radar precipitation estimates used in this work are kindly provided by the Met Office U.K.

8 REFERENCES Ashcroft, P., and F. Wentz. 2003, updated daily. /AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (Tb) V001 Capacci D. and B. J. Conway, 2005: Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks. Met. Appl. Accepted paper. Capacci D., Battaglia A., Conway B. J., Mantovani S., Porcù F., Prodi F., 2004: On the sensitivity of MODIS visible/1.6 m channels to radar-measured ground precipitation, Proc of 14 th International Conference on Clouds and Precipitation, Fair, C. A., P. K. James and P. Larke (1990) The United Kingdom weather radar network. Pp in: Weather Radar Networking, seminar on COST Project 73. Ed. C. G. Collier and M. Chapuis, Kluwer Academic Publishers, Dordrecht. EUR EN-FR. Francis P.N., D. Capacci and R. Saunders, Improving the Nimrod nowcasting system s satellite precipitation estimates by introducing the new SEVIRI channels. Proc of EUMETSAT 2006 Conference. Golding, B.W., 1998: Nimrod, a system for generating automated very short range forecasts. Meteor. Appl., 5, Harrison D.L., Driscol, S.J. and Kitchen, M. (2000). Improving precipitation estimates from weather radar using quality control and correction techniques. Meteorological Applications, 6, King, M.D., S. Tsay, S. E. Platnick, M. Wang and K.Liou, 1997: Cloud Retrieval Algorithm for MODIS: Optical Thickness, Effective Particle Radius and Thermodynamic Phase. MODIS Algorithm Theoretical Basis Document No. ATBD-MOD-05 MOD06, 79 pp. Joyce, R.J., J.E. Janowiak, P. A. Arkin and P. Xie (2004) CMORPH: A method that produces global precipitation estimated from passive microwave and infrared data at high spatial and temporal resolution. J. Appl. Meteorol., 5, Pankiewicz G. S., C. J. Johnson and D. L. Harrison, 2001, Improving radar observations of precipitation with a Meteosat neural network classifier. Meteorol. Atmos. Phys., 76, Rosenfeld, Daniel and Itamar M.Lensky, 1998: Satellite-based insights into precipitation formation processes in continental and maritime convective clouds. American Meteorological Society, 79, pg Rosenfeld, D., E. Cattani, S. Melani, and V. Levizzani, 2003: Considerations on daylight operation of 1.6 µm vs. 3.7 µm channels on NOAA and METOP satellites. Bull. Amer. Meteor. Soc., 85, Turk, F. J., G. Rohaly, J. Hawkins, E. A. Smith, F. S. Marzano, A. Mugnai, and V. Levizzani, 2000: Meteorological applications of precipitation estimation from combined SSM/I, TRMM and geostationary satellite data. Microwave Radiometry and Remote Sensing of the Earth s Surface and Atmosphere, P. Pampaloni and S. Paloscia Eds., VSP Int. Sci. Publisher, Utrecht (The Netherlands),