Gill, Rashpal S.

Hansen, Lars Kai

Jensen, Thomas B.S.

Nielsen, Allan Aasbjerg

Overgaard, Søren

This contribution presents the initial results from experiments with detection of weather radar clutter by information fusion with satellite based nowcasting products. Previous studies using information fusion of weather radar data and first generation Meteosat imagery have shown promising results for the detecting and removal of clutter. Naturally, the improved spatio-temporal resolution of the Meteosat Second Generation sensors, coupled with its increased number of spectral bands, is expected to yield even better detection accuracies. Weather radar data from three C-band Doppler weather radars of the Danish Meteorological Institute has been extracted for cases of severe to moderate cases of land and sea clutter. For comparison, cases of clutter free data has also been analyzed. The satellite-based dataset used is an operational meteorological product developed within the 'Nowcasting Satellite Application Facility' of EUMETSAT and is based on multispectral images from the SEVIRI sensor of the Meteosat-8 platform. Of special interest is the 'Precipitating Clouds' product, which uses the spectral information coupled with surface temperatures from Numerical Weather Predictions to assign probabilities for three rain intensity classes (heavy precipitation, light to moderate precipitation, and no precipitation) to every pixel in the Meteosat-8 images. Image fusion of the satellite image product and the radar composite images was done by resampling to a common grid of a spatial resolution dictated by the resolution of the radar data. Subsequently, a supervised classifier was developed based on training data selected by a weather radar expert. Results of classification of data from several different meteorological events are shown. Cases of widespread sea clutter caused by anomalous propagation are especially seen to be detected with high accuracy using the proposed method, however, the satellite based dataset overestimates precipitation which results in weather radar clutter being misclassified as precipitation. Other sources of errors were identified, such as geometrical and temporal misalignments arising from the use of multi-source datasets

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ERAD  2006Proceedings ofDetecting weather radar clutterusing satellite-based nowcasting productsT. Bøvith1,2, R. S. Gill1, S. Overgaard1, L. K. Hansen2, and A. A. Nielsen21Danish Meteorological Institute, Lyngbyvej 100, DK-2100 Copenhagen Ø, Denmark2Informatics and Mathematical Modelling, Technical University of Denmark, DK-2800 Lyngby, DenmarkAbstract. This contribution presents some initial resultsfrom investigations into detection of weather radar clutter bydata fusion with satellite-based nowcasting products.Weather radar data from three C-band Doppler weatherradars of the Danish Meteorological Institute has been ex-tracted for cases of sea and land clutter caused by anoma-lous propagation as well as cases of clutter free data. In ad-dition, an operational nowcasting product developed by theNowcasting Satellite Application Facility of EUMETSAT isused. The Precipitating Clouds product, which is based onMeteosat-8 data and auxiliary information from numericalweather predictions, provides probabilities of precipitationfor every pixel in a Meteosat-8 scene. Via pixel-level imagefusion of the radar data and the Precipitating Clouds product,supervised classification of the radar echoes into clutter andprecipitation classes is performed.Results from classification of three typical events areshown and discussed. Cases of moderate and severe groundclutter caused by anomalous propagation is detected withhigh accuracies. However, misclassification of radar echoesis observed both during no-clutter precipitation events andwhen precipitation areas are close to areas of clutter.1 IntroductionWeather radar clutter is a major contributor to degraded ra-dar data quality and is an obstacle for the many applicationsof weather radar data: For its traditional uses in forecastingand decision making and in particular for the use in auto-mated procedures and models incorporating radar data, e.g.hydrological, nowcasting and numerical weather predictionmodels.Correspondence to: T. Bøvith(thb@dmi.dk)Various methods for detection, mitigation, and removal ofclutter has been proposed (Steiner and Smith (2002) givesa good overview) ranging from low-level signal processingmethods over higher level pattern recognition to data fusionmethods and the use of new advances in radar technology(e.g. dual polarimetric radars). Traditional clutter detectionmethods using Doppler velocity filtering are capable of re-moving stationary land clutter, however, sea clutter and otherclutter originating from moving targets is difficult to detectusing single-polarized radar data alone.This contribution thus takes a data fusion approach, morespecifically image fusion of a new nowcasting product basedon mainly Meteosat-8 multispectral data. Previous studiesusing information fusion of weather radar data and first gen-eration Meteosat multispectral imagery have shown promis-ing results for the detecting and removal of clutter (Michel-son and Sunhede, 2004). Naturally, the improved spatio-temporal resolution of the Meteosat Second Generation sen-sors, coupled with its increased number of spectral bands,is expected to yield even better estimation of precipitation(Levizzani et al., 2001) and therefore potentially improvedclutter detection.2 Data2.1 Radar dataRadar volume data were retrieved for three of the fourweather radars of the Danish Meteorological Institute (DMI)located at Sindal, Rømø, and Stevns. All three radars are C-Band Doppler radars. The individual radar volumes wherepreprocessed into 2D images using an in-house DMI algo-rithm and then projected into a common mosaic for full cov-erage of all three radars (See Fig. 1, top row). The spatialresolution of the final radar mosaic is 1 km and the temporalresolution is 10 min.T. Bøvith et al.: Detecting weather radar clutter2.2 Nowcasting SAF productsMeasuring and mapping of precipitation from space-borneplatforms (in low-earth and geostationary orbits) can be per-formed using passive sensing of the visible and infrared partsof the electromagnetic spectrum as well as passive and activesensing in the microwave region (See Levizzani et al. (2001)for an overview of the techniques).For this study a new product developed by EUMET-SATs ’Nowcasting Satellite Application Facility’ (Nowcast-ing SAF) is used. A wide range of products are developedwithin the Nowcasting SAF, of which some are more or lessrelated to precipitation: Cloud masks, types, and height prod-ucts, and several precipitation products. Of the latter, thePrecipitating Clouds (PC) product is particularly interest-ing in relation to quality control of weather radar data. ThePrecipitating Clouds product provides probability values forthree precipitation classes for each pixel in the Meteosat-8scene: heavy, light to moderate and no precipitation. Thealgorithm behind the PC product uses a linear combinationof the spectral information of the Meteosat-8 bands togetherwith surface temperatures from a numerical weather predic-tion model (NWCSAF/PGE04, 2005). The PC data have a3 km spatial resolution at nadir (5 km at latitudes of Scandi-navia) and 15 minute temporal resolution.3 MethodThe data sets are combined by pixel-level image fusion (Pohland van Genderen, 1998) followed by supervised classifica-tion using a scale-space ensemble classifier as described byBøvith et al. (2006).3.1 Pixel level image fusionPixel-level image fusion involves geocoding and interpola-tion of the images to a common grid. Here a stereographicmap with a grid spacing of 1 km is chosen to match the res-olution of the radar data.The difference in temporal resolution of the data sets isdealt with in this simple way: For each radar image the clos-est matching nowcasting image in time is chosen. The maxi-mum temporal mismatch between the data sets is thus 5 minwhich at a velocity of clouds of e.g., 10 ms−1 will result in a3 km mismatch of the observed features. This corresponds tothree pixels in the final grid and could give rise to increasedmisclassification errors.The effects of the difference in temporal resolution was in-spected visually by superimposition of the fused images andsome disagreements between features were observed. Thereason for the mismatch, however is not only the temporalmisalignment but also the fact that the two datasets observedifferent phenomena; The radar measures backscatter fromthe precipitation itself whereas the satellite product is derivedfrom the reflected and emitted energy from cloud top.3.2 Scale-space ensemble methodSupervised classification is performed by extraction of man-ually selected training samples for precipitation and clutterclasses in the radar images. The training data are dividedinto two groups, one for building the classifiers and one forevaluation of the classification results by computation of theclassification error.Classification is done using a scale-space ensemble clas-sification method: Firstly, a scale-space representation of theinput features is created by convolution of the input featureswith a Gaussian kernel of a given window size correspondingto the scale. This blurs the input features which was shown inBøvith et al. (2006) to mitigate the influence of the misalign-ment between the fused images. Secondly, an ensemble clas-sification method (also called a multiple classifier method) isused in which several models for classification are combinedinto an improved classifier with better performance than clas-sification by use of each model individually.4 Example casesThree typical cases were processed using the method de-scribed above. Case I is a case of severe sea clutter and mod-erate land clutter without precipitation. Case II is a case ofsea clutter with precipitation and finally, Case III is a case ofno clutter during the passage of convective precipitation (SeeFig. 1).Case I. Clutter, no precipitation. 2006-05-05 20:10 UTCIn the months of spring, summer and fall in Denmark,anomalous propagation (AP) conditions are quite frequent,giving rise to weather radar clutter due to superrefraction andducting of the radar beams. Especially the radar at Stevns ex-periences AP due to its location very close to the coast line ofthe Baltic Sea where temperature inversions occur frequentlydue the flow of hot and moist air masses over the colder oceansurface.For more than a week starting around May 5, 2006, theweather in Denmark was characterized by clear air and highpressure with little precipitation. Pronounced sea clutter isseen over a large part of the Baltic Sea and moderate seaclutter is seen in the North Sea (See Fig. 1, column 1). Onall radars, close range land clutter is seen, especially on thenorthern most radar at Sindal. From the Meteosat-8 infraredband 4 and the Precipitating Clouds probability map it isseen that only a little precipitation was present (in the middleof the North Sea).The bottom image in column one in Fig. 1, shows the re-sult of classification of the radar echoes by image fusion withthe PC image. Perfect classification, i.e. a classification errorof zero, was obtained for this case. The classification error(reported in Tab. 1) was computed from the confusion matrixbetween training samples and validation samples.T. Bøvith et al.: Detecting weather radar clutterCASE I CASE II CASE IIIClutter, no precipitation. 2006-05-05 20:10 UTC Clutter and precipitation. 2005-09-25 20:00 UTC Precipitation, no clutter. 2006-05-19 10:40 UTCFig. 1. Case I–III. Rows (top down): 1) Radar reflectivity factor (blues are low reflectivities, greens and yellows are medium, orange andred hues are high reflectivities). Northern-most radar is Sindal, western is Rømø, eastern is Stevns 2) Meteosat-8 IR 3.9 µm (bright hues arelow brightness temperatures (clouds)) 3) Nowcasting SAF Precipitating Clouds (dark colors are higher probabilities of precipitating clouds,bright or white are lower probabilities) 4) Classification result (red are radar echoes classified as clutter, green are precipitation).T. Bøvith et al.: Detecting weather radar clutterTable 1. Classification error (validation samples misclassified) andthe Total Error (all pixels taken into consideration).Case Classification error rate Total errorI 0.00 % 0.00 %II 1.37 % 8.01 %III 0.94 % 3.26 %Case II. Clutter and precipitation. 2005-09-25 20:00 UTCThis case exemplifies the case of AP clutter echoes near pre-cipitation areas. Two precipitation areas stretching north-south coming in from south-west are noticed as well as alarge area of sea clutter in the Baltic Sea. The reason forthe AP conditions was a strong temperature inversion whichlasted throughout the day into the night. Severe AP clut-ter mixed with precipitation is not as common as AP clutteralone due to the meteorological conditions causing AP, how-ever the cases of mixed clutter and precipitation are morecomplex to detect.The classification result is not as good as in Case I; theclassification error is 1.37 % and the total error is 8.01 %.The total error is computed on the basis of all of the pix-els in the scene and not only the training/validation sampleswhich are selected in areas where there is no doubt upon thealignment of the two data sets (i.e. not on the edges of pre-cipitation and clutter areas). Clutter is seen to be classifiedvery well, however some precipitation on the edges of theprecipitation fronts is misclassified as clutter and close rangeland clutter on the radars at Sindal and Rømø is misclassifiedas precipitation.From Case II it is also noticed how the PrecipitatingClouds product contains accurate information on whichclouds are precipitating and which are not. For example, theclouds near the sea clutter area are not given very high prob-ability of precipitation and hence the clutter echoes off thecoast of Stevns are classified correctly as clutter. It can alsobe seen how the PC product has a tendency to overestimatethe amount of precipitating clouds which leads to misclassi-fication in areas of mixed precipitation and clutter.Case III. Precipitation, no clutter. 2006-05-19 10:40 UTCCase III shows an event of convective showers moving north-east extending over most of the radar coverage. No clutterwas observed during this event except for minor areas ofclose range ground clutter and some mid to far range landclutter on the coasts of Norway and Sweden on the northernmost radar at Sindal. This case was included for checkingthe detection method on clutter free data which is relevantfor the evaluation of the method in relation to its operationaluse. Ideally, all radar echoes in this case should be classifiedas precipitation. As seen in the classification result this is notthe case. The precipitation areas of the convective system areclassified as precipitation with a classification error of 0.94 %and a total error of 3.26 %. Areas of precipitation in the cen-tral part of Jutland are misclassified as clutter which is due tolow PC probabilities here. The problems of misclassificationat the edges of precipitation is not seen in this case.5 ConclusionsThe initial results of detecting weather radar clutter by pixel-level image fusion with Precipitating Clouds, an operationalnowcasting product based on satellite data, is presented.The method uses supervised classification and the resultsfrom three typical cases is shown. Classification error ratesfrom 0.00 to 1.37 % (0.00 to 8.01 % total error) are achieveddepending on the complexity of the scene. Cases of sea andland clutter caused by anomalous propagation are detectedwith high accuracies, especially for the case of no precipi-tation in the vicinity of the clutter. Misclassification occurswhen precipitation is near areas of clutter and in the case ofprecipitation and no clutter.The satellite-based dataset overestimates precipitationwhich results in clutter being misclassified as precipitation.Other sources of errors stems from the different nature of themulti-source data. The method is generally capable of de-tecting clutter with a high accuracy at the expense of somemisclassification of precipitation.Acknowledgements. EUMETSAT and the Nowcasting SAF is ac-knowledged for the operational nowcasting SAF products.ReferencesBøvith, T., Nielsen, A. A., Hansen, L. K., Overgaard, S., andGill, R. S.: Detecting Weather Radar Clutter by Information Fu-sion With Satellite Images and Numerical Weather PredictionModel Output, accepted for publication, in: Proceedings of theIEEE International Geoscience and Remote Sensing Symposium(IGARSS) 2006, Denver, Colorado, USA, 2006.Levizzani, V., Schmetz, J., Lutz, H., Kerkmann, J., Alberoni, P. P.,and Cervino, M.: Precipitation estimations from geostationaryorbit and prospects for METEOSAT Second Generation, Meteo-rological Applications, 8, 23–41, 2001.Michelson, D. B. and Sunhede, D.: Spurious weather radar echoidentification and removal using multisource temperature infor-mation, Meteorological Applications, 11, 1–14, 2004.NWCSAF/PGE04: Software User Manual for PGE04 of theNWCSAF/MSG: Scientific Part, Satellite Application Facility toNoWCasting and Very Short Range Forecasting 1.2, EUMET-SAT, 2005.Pohl, C. and van Genderen, J. L.: Multisensor image fusion in re-mote sensing: concepts, methods and applications, InternationalJournal of Remote Sensing, 19, 823–854, 1998.Steiner, M. and Smith, J. A.: Use of three-dimensional reflectivitystructure for automated detection and removal of nonprecipitat-ing echoes in radar data, Journal of Atmospheric and OceanicTechnology, 19, 673–686, 2002.

Detecting weather radar clutter using satellite-based nowcasting products

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Detecting weather radar clutter using satellite-based nowcasting products

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